{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "# Feature transformations with ensembles of trees\n",
    "\n",
    "Transform your features into a higher dimensional, sparse space. Then train a\n",
    "linear model on these features.\n",
    "\n",
    "First fit an ensemble of trees (totally random trees, a random forest, or\n",
    "gradient boosted trees) on the training set. Then each leaf of each tree in the\n",
    "ensemble is assigned a fixed arbitrary feature index in a new feature space.\n",
    "These leaf indices are then encoded in a one-hot fashion.\n",
    "\n",
    "Each sample goes through the decisions of each tree of the ensemble and ends up\n",
    "in one leaf per tree. The sample is encoded by setting feature values for these\n",
    "leaves to 1 and the other feature values to 0.\n",
    "\n",
    "The resulting transformer has then learned a supervised, sparse,\n",
    "high-dimensional categorical embedding of the data.\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from sklearn import set_config\n",
    "set_config(display='diagram')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "First, we will create a large dataset and split it into three sets:\n",
    "\n",
    "- a set to train the ensemble methods which are later used to as a feature\n",
    "  engineering transformer;\n",
    "- a set to train the linear model;\n",
    "- a set to test the linear model.\n",
    "\n",
    "It is important to split the data in such way to avoid overfitting by leaking\n",
    "data.\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = make_classification(n_samples=80000, random_state=10)\n",
    "\n",
    "X_full_train, X_test, y_full_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.5, random_state=10)\n",
    "X_train_ensemble, X_train_linear, y_train_ensemble, y_train_linear = \\\n",
    "    train_test_split(X_full_train, y_full_train, test_size=0.5,\n",
    "                     random_state=10)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "For each of the ensemble methods, we will use 10 estimators and a maximum\n",
    "depth of 3 levels.\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "n_estimators = 10\n",
    "max_depth = 3"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "First, we will start by training the random forest and gradient boosting on\n",
    "the separated training set\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "random_forest = RandomForestClassifier(\n",
    "    n_estimators=n_estimators, max_depth=max_depth, random_state=10)\n",
    "random_forest.fit(X_train_ensemble, y_train_ensemble)\n",
    "\n",
    "gradient_boosting = GradientBoostingClassifier(\n",
    "    n_estimators=n_estimators, max_depth=max_depth, random_state=10)\n",
    "_ = gradient_boosting.fit(X_train_ensemble, y_train_ensemble)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The :class:`~sklearn.ensemble.RandomTreesEmbedding` is an unsupervised method\n",
    "and thus does not required to be trained independently.\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "from sklearn.ensemble import RandomTreesEmbedding\n",
    "\n",
    "random_tree_embedding = RandomTreesEmbedding(\n",
    "    n_estimators=n_estimators, max_depth=max_depth, random_state=0)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, we will create three pipelines that will use the above embedding as\n",
    "a preprocessing stage.\n",
    "\n",
    "The random trees embedding can be directly pipelined with the logistic\n",
    "regression because it is a standard scikit-learn transformer.\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "rt_model = make_pipeline(\n",
    "    random_tree_embedding, LogisticRegression(max_iter=1000))\n",
    "rt_model.fit(X_train_linear, y_train_linear)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style>#sk-6433c181-16c6-44a7-8765-2931e76214fe {color: black;background-color: white;}#sk-6433c181-16c6-44a7-8765-2931e76214fe pre{padding: 0;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-toggleable {background-color: white;}#sk-6433c181-16c6-44a7-8765-2931e76214fe label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-6433c181-16c6-44a7-8765-2931e76214fe input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6433c181-16c6-44a7-8765-2931e76214fe input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-estimator:hover {background-color: #d4ebff;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-item {z-index: 1;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-parallel-item:only-child::after {width: 0;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-6433c181-16c6-44a7-8765-2931e76214fe div.sk-container {display: inline-block;position: relative;}</style><div id=\"sk-6433c181-16c6-44a7-8765-2931e76214fe\" class\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4425dc2b-f387-4fc0-b35b-1c63ea925b13\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4425dc2b-f387-4fc0-b35b-1c63ea925b13\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('randomtreesembedding',\n",
       "                 RandomTreesEmbedding(max_depth=3, n_estimators=10,\n",
       "                                      random_state=0)),\n",
       "                ('logisticregression', LogisticRegression(max_iter=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b0340331-fdf2-4af5-9ddb-3d1a7d25c772\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b0340331-fdf2-4af5-9ddb-3d1a7d25c772\">RandomTreesEmbedding</label><div class=\"sk-toggleable__content\"><pre>RandomTreesEmbedding(max_depth=3, n_estimators=10, random_state=0)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"43400b89-9d21-4d75-835f-6ab36567cb18\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"43400b89-9d21-4d75-835f-6ab36567cb18\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('randomtreesembedding',\n",
       "                 RandomTreesEmbedding(max_depth=3, n_estimators=10,\n",
       "                                      random_state=0)),\n",
       "                ('logisticregression', LogisticRegression(max_iter=1000))])"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Then, we can pipeline random forest or gradient boosting with a logistic\n",
    "regression. However, the feature transformation will happen by calling the\n",
    "method `apply`. The pipeline in scikit-learn expects a call to `transform`.\n",
    "Therefore, we wrapped the call to `apply` within a `FunctionTransformer`.\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "\n",
    "def rf_apply(X, model):\n",
    "    return model.apply(X)\n",
    "\n",
    "\n",
    "rf_leaves_yielder = FunctionTransformer(\n",
    "    rf_apply, kw_args={\"model\": random_forest})\n",
    "\n",
    "rf_model = make_pipeline(\n",
    "    rf_leaves_yielder, OneHotEncoder(handle_unknown=\"ignore\"),\n",
    "    LogisticRegression(max_iter=1000))\n",
    "rf_model.fit(X_train_linear, y_train_linear)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style>#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b {color: black;background-color: white;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b pre{padding: 0;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-toggleable {background-color: white;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-estimator:hover {background-color: #d4ebff;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-item {z-index: 1;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-parallel-item:only-child::after {width: 0;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-01650b14-01da-4d1d-827b-9b2b5b433a7b div.sk-container {display: inline-block;position: relative;}</style><div id=\"sk-01650b14-01da-4d1d-827b-9b2b5b433a7b\" class\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"04374ddc-4781-4775-9e41-59a6e911123d\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"04374ddc-4781-4775-9e41-59a6e911123d\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('functiontransformer',\n",
       "                 FunctionTransformer(func=<function rf_apply at 0x7fa37866a1e0>,\n",
       "                                     kw_args={'model': RandomForestClassifier(max_depth=3,\n",
       "                                                                              n_estimators=10,\n",
       "                                                                              random_state=10)})),\n",
       "                ('onehotencoder', OneHotEncoder(handle_unknown='ignore')),\n",
       "                ('logisticregression', LogisticRegression(max_iter=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"759df25c-1344-4696-895f-052b1381ff0c\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"759df25c-1344-4696-895f-052b1381ff0c\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=<function rf_apply at 0x7fa37866a1e0>,\n",
       "                    kw_args={'model': RandomForestClassifier(max_depth=3,\n",
       "                                                             n_estimators=10,\n",
       "                                                             random_state=10)})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4828e412-83db-4be0-89a9-a63ad1d9b902\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"4828e412-83db-4be0-89a9-a63ad1d9b902\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"9989bf00-1e8b-4df8-a13a-ba005532fd94\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"9989bf00-1e8b-4df8-a13a-ba005532fd94\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('functiontransformer',\n",
       "                 FunctionTransformer(func=<function rf_apply at 0x7fa37866a1e0>,\n",
       "                                     kw_args={'model': RandomForestClassifier(max_depth=3,\n",
       "                                                                              n_estimators=10,\n",
       "                                                                              random_state=10)})),\n",
       "                ('onehotencoder', OneHotEncoder(handle_unknown='ignore')),\n",
       "                ('logisticregression', LogisticRegression(max_iter=1000))])"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "def gbdt_apply(X, model):\n",
    "    return model.apply(X)[:, :, 0]\n",
    "\n",
    "\n",
    "gbdt_leaves_yielder = FunctionTransformer(\n",
    "    gbdt_apply, kw_args={\"model\": gradient_boosting})\n",
    "\n",
    "gbdt_model = make_pipeline(\n",
    "    gbdt_leaves_yielder, OneHotEncoder(handle_unknown=\"ignore\"),\n",
    "    LogisticRegression(max_iter=1000))\n",
    "gbdt_model.fit(X_train_linear, y_train_linear)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/html": [
       "<style>#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 {color: black;background-color: white;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 pre{padding: 0;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-toggleable {background-color: white;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.2em 0.3em;box-sizing: border-box;text-align: center;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;margin: 0.25em 0.25em;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-estimator:hover {background-color: #d4ebff;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-item {z-index: 1;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-parallel-item:only-child::after {width: 0;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0.2em;box-sizing: border-box;padding-bottom: 0.1em;background-color: white;position: relative;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-6110cee5-196f-4f9f-a7f0-f1146681b230 div.sk-container {display: inline-block;position: relative;}</style><div id=\"sk-6110cee5-196f-4f9f-a7f0-f1146681b230\" class\"sk-top-container\"><div class=\"sk-container\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"907ee89d-0af5-4223-afeb-550dfe2855bf\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"907ee89d-0af5-4223-afeb-550dfe2855bf\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[('functiontransformer',\n",
       "                 FunctionTransformer(func=<function gbdt_apply at 0x7fa37866a400>,\n",
       "                                     kw_args={'model': GradientBoostingClassifier(n_estimators=10,\n",
       "                                                                                  random_state=10)})),\n",
       "                ('onehotencoder', OneHotEncoder(handle_unknown='ignore')),\n",
       "                ('logisticregression', LogisticRegression(max_iter=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"da6c5bfa-4ed3-4683-8404-e332aad01a30\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"da6c5bfa-4ed3-4683-8404-e332aad01a30\">FunctionTransformer</label><div class=\"sk-toggleable__content\"><pre>FunctionTransformer(func=<function gbdt_apply at 0x7fa37866a400>,\n",
       "                    kw_args={'model': GradientBoostingClassifier(n_estimators=10,\n",
       "                                                                 random_state=10)})</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"b9de2d15-1fa7-4ab9-b2ec-000d62b2529e\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"b9de2d15-1fa7-4ab9-b2ec-000d62b2529e\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown='ignore')</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e7afa951-9163-41b8-8fc6-7f1a9235993b\" type=\"checkbox\" ><label class=\"sk-toggleable__label\" for=\"e7afa951-9163-41b8-8fc6-7f1a9235993b\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('functiontransformer',\n",
       "                 FunctionTransformer(func=<function gbdt_apply at 0x7fa37866a400>,\n",
       "                                     kw_args={'model': GradientBoostingClassifier(n_estimators=10,\n",
       "                                                                                  random_state=10)})),\n",
       "                ('onehotencoder', OneHotEncoder(handle_unknown='ignore')),\n",
       "                ('logisticregression', LogisticRegression(max_iter=1000))])"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can finally show the different ROC curves for all the models.\n",
    "\n"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import plot_roc_curve\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "models = [\n",
    "    (\"RT embedding -> LR\", rt_model),\n",
    "    (\"RF\", random_forest),\n",
    "    (\"RF embedding -> LR\", rf_model),\n",
    "    (\"GBDT\", gradient_boosting),\n",
    "    (\"GBDT embedding -> LR\", gbdt_model),\n",
    "]\n",
    "\n",
    "model_displays = {}\n",
    "for name, pipeline in models:\n",
    "    model_displays[name] = plot_roc_curve(\n",
    "        pipeline, X_test, y_test, ax=ax, name=name)\n",
    "_ = ax.set_title('ROC curve')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "fig, ax = plt.subplots()\n",
    "for name, pipeline in models:\n",
    "    model_displays[name].plot(ax=ax)\n",
    "\n",
    "ax.set_xlim(0, 0.2)\n",
    "ax.set_ylim(0.8, 1)\n",
    "_ = ax.set_title('ROC curve (zoomed in at top left)')"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.6.8 64-bit ('base': conda)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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